Affiliations: [a] Department of Mechatronic and Robotic Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Johor, Malaysia. E-mail: arhisyam@uthm.edu.my | [b] Malaysian-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia. E-mail: xiangkk@gmail.com | [c] Centre of Artificial Intelligence and Robotics (CAIRO), Unversiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia. E-mail: cfyeong@utm.my | [d] Department of Electronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia. E-mail: eileensu@utm.my | [e] IJN-UTM Cardiovascular Engineering Center, Faculty of Biosciences & Medical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia. E-mail: aqilah@biomedical.utm.my
Abstract: Background:Clinical scales such as Fugl-Meyer Assessment (FMA) and Motor Assessment Scale (MAS) are widely used to evaluate stroke patient’s motor performance. However, there are several limitations with these assessment scales such as subjectivity, lack of repeatability, time-consuming and highly depend on the ability of the physiotherapy. In contrast, robot-based assessments are objective, repeatable, and could potentially reduce the assessment time. However, robot-based assessments are not as well established as conventional assessment scale and the correlation to conventional assessment scale is unclear. Objective:This study was carried out to identify important parameters in designing tasks that efficiently assess hand function of stroke patients and to quantify potential benefits of robotic assessment modules to predict the conventional assessment score with iRest. Methods:Twelve predictive variables were explored, relating to movement time, velocity, strategy, accuracy and smoothness from three robotic assessment modules which are Draw I, Draw Diamond and Draw Circle. Regression models using up to four predictors were developed to describe the MAS. Results:Results show that the time given should be not too long and it would affect the trajectory error. Besides, result also shows that it is possible to use iRest in predicting MAS score. Conclusion:There is a potential of using iRest, a non-motorized device in predicting MAS score.
